• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.577-580
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• 2002

인터넷상의 정보의 급증에 따라 필요한 정보를 발견하고 관련된 정보를 조직화하기가 더욱 어려워지고 있으며 정보 접근의 부하를 줄이기 위한 효율적인 문서 분류의 중요성 및 필요성이 증가하고 있다. 본 논문에서는 디렉토리 내의 학습 문서 집합을 기반으로 구성된 디렉토리 내의 대표 용어 집합으로 구성된 모델을 학습 및 분류하기 위해 SVM을 사용한다. 문서분류를 위해 정보통신 웹 디렉토리 내의 문서로부터 추출된 용어 집합을 기반으로 학습을 수행한 후 문서 분류를 수행한다. 또한 TFiDF를 기반으로 특징을 표현하기 위해 벡터공간 모델을 사용하였고 이를 기반으로 성능 평가를 수행한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1535-1538
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• 2003

웹 상의 데이터 교환 표준으로 XML이 등장한 이래 XML 데이터를 관계 DBMS에 저장하고 질의 처리를 수행하는 기법에 관한 연구가 활발히 수행되고 있다. 전통적인 데이터베이스에서처럼 단일 또는 복수개의 XML문서에 대한 XML 질의를 통해 XML 뷰를 정의할 수 있고 이는 질의 처리 성능 향상을 위해 웹 환경에서 실체화될 수 있다. 본 논문에서는 XML 데이터를 관계 DBMS에 저장하였을 때 XML 질의의 결과를 실체뷰로 캐쉬하였다가 동일 질의가 반복될 때 캐쉬를 이용한 응답을 수행하기 위한 XML 뷰 실체화 모델을 세 가지 제시하고 비교, 평가한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.566-568
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• 1999

본 논문에서는 실시간 시스템의 시간 제약이 제대로 만족되는가를 시스템 수행 중에 감시하는 수행시간 모니터를 구현하기 위한 환경으로 실시간 객체 모델인 TMO(Time-triggered Message-triggered Object) 모델과 Windows 98NT 상의 TMO 프로그램 실행 환경인 WTMOS(Windows TMO System)를 이용한다. 모니터의 대상과 모니터하는 조건을 TMO 프로그램에 명시하는 방법이 연구되었고 정의된 모니터 기능을 WTMOS 내부와 TMO 형태의 응용 객체로 분산시켜서 적은 비용으로 모니터 시스템을 구축하는 효과적인 방법이 개발되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.151-152
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• 2009

Grid 컴퓨팅 환경에서 Risk-resilient 한 Job 수행을 보장하기 위해 그 동안 Job migration 기법이 연구되어 왔으나, 자원 재선정 및 Job 이동/재할당에 따른 기준의 단순성으로 인해, Migration에 따른 Job 수행의 이득과 손실이 정확하게 판별되지 못한 경향이 있었다. 따라서 본고에서는 Job failure Rate을 바탕으로 특정 Job의 확률적 수행 지연 시간을 추정하고, 이를 이용하여 Migration gain을 평가하는 모델을 제안한다.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05d
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• pp.386-391
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• 1996

동경전력(주)가 도쿄만 근처에 건설한 높이-직경비가 비교적 큰 지진시험모델(TEPSCO모델)에 수행한 강제진동시험결과와 시험모델의 지진응답을 해석하고 분석하였다. 재료시험과 지반조사 결과를 이용하여 예측해석모델을 만들었고, 시험시에 계측된 측정치를 반영하여 예측해석모델을 수정하여 예측후상관해석모델을 작성하였다. 해석은 진동수영역과 시간영역에서 각각 이루어졌다. 연구결과로 TEPSCOT모델의 경우는 부지와 시험모델의 형상특성으로 인하여 지반의 재료감쇠비가 동적응답에 미치는 영향이 지배적이었음을 알 수 있었다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.1105-1111
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• 2010

The Korea Atomic Energy Research Institutes has been developing the SMART (System integrated Modular Advanced ReacTor), an environment-friendly nuclear reactor for the generation of electricity and to perform desalination. SMART reactors can be exposed to various external and internal loads caused by seismic and coolant flows. The CRDM(control rod drive mechanism), one of structures of the SMART, is a component which is adjusting inserting amount of a control rod, controlling output of reactor power and in an emergency situation, inserting a control rod to stop the reactor. The purpose of this research is performing the analysis of dynamic characteristic to ensure safety and integrity of structure of CRDM. This paper presents two FE-models, 3-D solid model and simplified Beam model of the CRDM in the coolant, and then compared the results of the dynamic characteristic about the two FE-models using a commercial Finite Element tool, ABAQUS CAE V6.8 and ANSYS V12. Beam 4 and beam 188 of simplified-model were also compared each other. And simplified model is updated for accuracy compare to 3-D solid.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.4
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• pp.47-60
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• 1999

The stability and efficiency of structural control systems depend on the accuracy of mathematical model of the system to be controlled. In this study, state equation models of a small scale test structure and an AMD(active mass damper) are obtained separately using OKID(observer/Kalman filter identification) which is a time domain system identification method. The test structure with each floor acceleration as outputs is identified for two inputs - the ground acceleration and the acceleration of the moving mass of AMD relative to the installation floor - individually and the two identified state equation models are integrated into one by model reduction method. The AMD is identified with the motor control signal as an input and the relative acceleration of the moving mass as an output, and it is shown that the identified model has large damping ratio and phase shift. The transfer functions and the time histories reconstructed from the identified models of the test model and the AMD match well with those measured from the experiment.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.104-116
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• 2024

A satellite is ultimately verified by performing a coupled load analysis with the launch vehicle. To increase the accuracy of the coupled load analysis results, it is important to have good accuracy of the finite element model. Therefore, finite element model correlation is essential. In general, model correlation is performed by changing the material properties and thickness one by one, but this process takes a lot of time and cost. The current paper proposes an efficient model correlation method using optimization. Significant variables were selected through analysis of variance, and the time and cost required for analysis and optimization were reduced by using the Kriging surrogate model. The method proposed in this paper can be applied only with the vibration test results, and it has a great advantage in terms of efficiency in that it can significantly reduce the numerical calculation cost and time required.